code: def identity_statistics(x, mask, dim=-1, eps=1e-6):
      """Identity Scaler
  
      A placeholder identity scaler, that is argument insensitive.
  
      **Parameters:**<br>
      `x`: torch.Tensor input tensor.<br>
      `mask`: torch Tensor bool, same dimension as `x`, indicates where `x` is valid and False
              where `x` should be masked. Mask should not be all False in any column of
              dimension dim to avoid NaNs from zero division.<br>
      `eps` (float, optional): Small value to avoid division by zero. Defaults to 1e-6.<br>
      `dim` (int, optional): Dimension over to compute median and mad. Defaults to -1.<br>
  
      **Returns:**<br>
      `x`: original torch.Tensor `x`.
      """
      # Collapse dim dimension
      shape = list(x.shape)
      shape[dim] = 1
  
      x_shift = torch.zeros(shape)
      x_scale = torch.ones(shape)
  
      return x_shift, x_scale
  
imports: import torch
  import torch.nn
label_desc: Use image node_id 18 for calling a global function with example usage: identity_statistics(x, mask, dim, eps) and returns: x_shift,  x_scale